Robert Sellers spent 25 years inside regulated finance before founding KAiM, where he builds the accountability layer for business AI.
That biography is the point. He worked the supervisory side, where the regulator decides what an institution must be able to prove. He worked the operating side, where a bank has to actually produce that proof, every day, under pressure. Few people have stood in both places. He has.
So when AI started making real decisions inside businesses — approvals, denials, recommendations that move money and affect people — he saw the gap before most. The institutions he came up in spent a century building the machinery of accountability: controls before an action, evidence after it, clear limits on who's allowed to do what. AI was being deployed with almost none of it.
KAiM closes that gap. The principle is simple and it doesn't bend: AI proposes. Deterministic evaluators enforce. AI can suggest. Something deterministic checks it against your rules before it goes out the door. You're left with a clean record you can defend.
Built on three decades of making complex systems answerable.
A career across the Federal Reserve, a top-four U.S. bank, and the global firms where enterprise standards get set — leading large, regulated technology and governance programs, the largest north of $30M, with four U.S. patents along the way. The throughline never changed: making complex systems answerable.
Prefer the full chronology? It's on request, or on LinkedIn.
KAiM is the accountability layer for business AI — controls before the action, evidence after, bounded authority throughout.